{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:KR3DIFFR224RN4VHSY4TVLEO3E","short_pith_number":"pith:KR3DIFFR","schema_version":"1.0","canonical_sha256":"54763414b1d6b916f2a796393aac8ed92fe6c83f118501c8357f88bb5b5615d5","source":{"kind":"arxiv","id":"2605.31025","version":1},"attestation_state":"computed","paper":{"title":"TRACE: Discovering Task-Specific Parameter via Adaptation-Aware Probing for Continual Fine-Tuning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Di Liang, Fausto Giunchiglia, Ke Chen, Minlong Peng, Renchu Guan, Wei Pang, Xiaosong Han, Xiaoyue Feng, Xindi Dai, Yonghao Liu","submitted_at":"2026-05-29T08:57:06Z","abstract_excerpt":"In real-world deployment, LLMs are often adapted continually across tasks to keep LLMs up-to-date in production, where new fine-tuning should preserve previously learned skills. However, indiscriminately mixing tasks can dilute task specialization, while sequential fine-tuning (full-parameter or low rank adaptation) often causes catastrophic forgetting due to destructive overwriting. Replay-based continual tuning and maintaining separate task-specific adapters can mitigate forgetting, but introduce additional compute, storage, and management overhead. Recognizing the redundancy of LLM paramete"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2605.31025","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-05-29T08:57:06Z","cross_cats_sorted":[],"title_canon_sha256":"28f56be2c568066d2f7898bb13c91922181ac01aaf6f0ba264ddaf4427333a24","abstract_canon_sha256":"8fc5c209b3d7ed85115e42eff434b8e3c4ab249d70273a6cd53a9c8598321a93"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-01T01:03:31.401235Z","signature_b64":"6xDjhw4Iltxcb6eX1/4py+Q9a8hKPvHAnRZJoH0MQPOmAlVQLq1k3Y8EmznrYvmRiHFp733nim0sFrxGeyx3Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"54763414b1d6b916f2a796393aac8ed92fe6c83f118501c8357f88bb5b5615d5","last_reissued_at":"2026-06-01T01:03:31.400138Z","signature_status":"signed_v1","first_computed_at":"2026-06-01T01:03:31.400138Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TRACE: Discovering Task-Specific Parameter via Adaptation-Aware Probing for Continual Fine-Tuning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Di Liang, Fausto Giunchiglia, Ke Chen, Minlong Peng, Renchu Guan, Wei Pang, Xiaosong Han, Xiaoyue Feng, Xindi Dai, Yonghao Liu","submitted_at":"2026-05-29T08:57:06Z","abstract_excerpt":"In real-world deployment, LLMs are often adapted continually across tasks to keep LLMs up-to-date in production, where new fine-tuning should preserve previously learned skills. However, indiscriminately mixing tasks can dilute task specialization, while sequential fine-tuning (full-parameter or low rank adaptation) often causes catastrophic forgetting due to destructive overwriting. Replay-based continual tuning and maintaining separate task-specific adapters can mitigate forgetting, but introduce additional compute, storage, and management overhead. Recognizing the redundancy of LLM paramete"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.31025","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.31025/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2605.31025","created_at":"2026-06-01T01:03:31.400313+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.31025v1","created_at":"2026-06-01T01:03:31.400313+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.31025","created_at":"2026-06-01T01:03:31.400313+00:00"},{"alias_kind":"pith_short_12","alias_value":"KR3DIFFR224R","created_at":"2026-06-01T01:03:31.400313+00:00"},{"alias_kind":"pith_short_16","alias_value":"KR3DIFFR224RN4VH","created_at":"2026-06-01T01:03:31.400313+00:00"},{"alias_kind":"pith_short_8","alias_value":"KR3DIFFR","created_at":"2026-06-01T01:03:31.400313+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KR3DIFFR224RN4VHSY4TVLEO3E","json":"https://pith.science/pith/KR3DIFFR224RN4VHSY4TVLEO3E.json","graph_json":"https://pith.science/api/pith-number/KR3DIFFR224RN4VHSY4TVLEO3E/graph.json","events_json":"https://pith.science/api/pith-number/KR3DIFFR224RN4VHSY4TVLEO3E/events.json","paper":"https://pith.science/paper/KR3DIFFR"},"agent_actions":{"view_html":"https://pith.science/pith/KR3DIFFR224RN4VHSY4TVLEO3E","download_json":"https://pith.science/pith/KR3DIFFR224RN4VHSY4TVLEO3E.json","view_paper":"https://pith.science/paper/KR3DIFFR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.31025&json=true","fetch_graph":"https://pith.science/api/pith-number/KR3DIFFR224RN4VHSY4TVLEO3E/graph.json","fetch_events":"https://pith.science/api/pith-number/KR3DIFFR224RN4VHSY4TVLEO3E/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KR3DIFFR224RN4VHSY4TVLEO3E/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KR3DIFFR224RN4VHSY4TVLEO3E/action/storage_attestation","attest_author":"https://pith.science/pith/KR3DIFFR224RN4VHSY4TVLEO3E/action/author_attestation","sign_citation":"https://pith.science/pith/KR3DIFFR224RN4VHSY4TVLEO3E/action/citation_signature","submit_replication":"https://pith.science/pith/KR3DIFFR224RN4VHSY4TVLEO3E/action/replication_record"}},"created_at":"2026-06-01T01:03:31.400313+00:00","updated_at":"2026-06-01T01:03:31.400313+00:00"}